Databases are computerized information storage and retrieval systems. A Relational Database Management System (RDBMS) is a database management system (DBMS) which uses relational techniques for storing and retrieving data. Relational databases are organized into tables. A database will typically have many tables which are stored on random access storage devices (RASD), such as magnetic or optical disk drives for semi-permanent storage.
In recent years, DBMSs have become increasingly popular for several factors, including the decrease in the cost of storage devices and the increased need to store and track electronic information. As DBMSs become increasingly popular, more and more data is stored in databases, and handling, storing, analysing, archiving, moving, and collating the data becomes more of a concern to those utilizing such data.
To manage this increasing data load various software aids, sometimes referred to as utilities, have been developed. One thing that utilities have in common is that utilities assist users in managing data. They may be simple, such as a back-up utility, which merely copies several files. They may be more complex, such as a structured query language interface, which has evolved into a standardized mechanism for manipulating data. They also may be complex and sophisticated on-line analytical processing programs (OLAP) which are designed to do complex analytical processing.
Different types of data, relational and object, may be stored in data warehouses. The term “data warehouse” is used to describe large amounts of related data that are stored together. With the increased data that is stored, there has been an increasing complexity in using, retrieving, sorting and organizing data.
On-line analytical processing (OLAP) is a key part of most data warehouse and business analysis systems. OLAP services provide for fast analysis of multi-dimensional information. For this purpose, OLAP services provide for multi-dimensional access and navigation of data in an intuitive and natural way, providing a global view of data that may be drilled down into particular data of interest. Speed and response time are important attributes of OLAP services that allow users to browse and analyze data on-line in an efficient manner. Further, OLAP services typically provide analytical tools to rank, aggregate, and calculate lead and lag indicators for the data under analysis.
An OLAP cube is a multi-dimensional representation of a set of data. Such a cube is the basis for transaction data storage in conventional data warehouse systems.
The SAP business information warehouse is a data warehouse system, which enables the analyzing of data from operative SAP applications as well as other business applications, and external data sources such as databases, on-line services, and the Internet. The SAP business information warehouse enables OLAP for processing of information from large amounts of operative and historical data. In this context, OLAP technology enables multi-dimensional analysis from various business perspectives.
The business information warehouse server for core areas and processes, pre-configured with business content, ensures that a user may look at information within the entire enterprise. In selected roles in a company, business content offers the information that employees need to carry out their tasks. As well as roles, business content contains other pre-configured objects such as cubes, queries, key figures, and characteristics for simplification of business information warehouse implementation.
With the business explorer, the SAP business information warehouse provides flexible reporting and analysis tools for analysis and decision-making support. These tools include query, reporting, and OLAP functions. An employee having access authorization may evaluate past or current data on various levels of detail and from different perspectives, not only on the web but also in Microsoft Excel.
WO OO/19340 shows a multi-dimensional data management system. Multi-dimensional data is organized into classes, which correspond to each of the dimensions that characterize the data. All relevant data is consolidated into a fact table, which is based upon information of interest. The data within this table is linked to the top level of each class that corresponds to a different dimension of data, and sub-classes, which exist within a given dimension of class automatically, inherit the linked reference to the consolidated data. A user may thereby select search criteria within particular classes that correspond to dimensions of interest. This search criteria is then used to form a query which is applied to a relational database to obtain the desired results.
U.S. Pat. No. 5,978,788 shows a system and method for generating multi-representations of a data cube. The data cube is split into a plurality of dimensions. Representations are selected and the data cube is reconstructed.
U.S. Pat. No. 6,418,427 shows a method for modifying dimension structures and relations in multi-dimensional processing.
U.S. 2003/0023608A1 shows a method for transforming a set of relations into multi-dimensional data cubes by means of a synthesis process.
It is a common disadvantage of conventional data processing systems that the assignment of data tables to be processed by application programs and the selection of the output format of the data processing is a tedious and error prone task which often involves a substantial amount of manual interaction. There is, therefore, a need for data processing systems and methods that reduces the amount of user interaction.